Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory610.2 KiB
Average record size in memory624.9 B

Variable types

Text1
Categorical7
Numeric6
Unsupported2

Alerts

gross margin percentage has constant value "4.761904762" Constant
Branch is highly overall correlated with CityHigh correlation
City is highly overall correlated with BranchHigh correlation
Quantity is highly overall correlated with Sales and 2 other fieldsHigh correlation
Sales is highly overall correlated with Quantity and 3 other fieldsHigh correlation
Unit price is highly overall correlated with Sales and 2 other fieldsHigh correlation
cogs is highly overall correlated with Quantity and 3 other fieldsHigh correlation
gross income is highly overall correlated with Quantity and 3 other fieldsHigh correlation
Invoice ID has unique values Unique
Date is an unsupported type, check if it needs cleaning or further analysis Unsupported
Time is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-09-03 23:54:30.835024
Analysis finished2025-09-03 23:54:36.739532
Duration5.9 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Invoice ID
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
2025-09-04T02:54:37.301877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row750-67-8428
2nd row226-31-3081
3rd row631-41-3108
4th row123-19-1176
5th row373-73-7910
ValueCountFrequency (%)
665-32-9167 1
 
0.1%
849-09-3807 1
 
0.1%
750-67-8428 1
 
0.1%
226-31-3081 1
 
0.1%
631-41-3108 1
 
0.1%
123-19-1176 1
 
0.1%
373-73-7910 1
 
0.1%
699-14-3026 1
 
0.1%
189-40-5216 1
 
0.1%
374-38-5555 1
 
0.1%
Other values (990) 990
99.0%
2025-09-04T02:54:37.849617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2000
18.2%
2 957
8.7%
6 954
8.7%
1 950
8.6%
8 944
8.6%
5 927
8.4%
4 918
8.3%
3 909
8.3%
7 895
8.1%
0 809
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2000
18.2%
2 957
8.7%
6 954
8.7%
1 950
8.6%
8 944
8.6%
5 927
8.4%
4 918
8.3%
3 909
8.3%
7 895
8.1%
0 809
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2000
18.2%
2 957
8.7%
6 954
8.7%
1 950
8.6%
8 944
8.6%
5 927
8.4%
4 918
8.3%
3 909
8.3%
7 895
8.1%
0 809
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2000
18.2%
2 957
8.7%
6 954
8.7%
1 950
8.6%
8 944
8.6%
5 927
8.4%
4 918
8.3%
3 909
8.3%
7 895
8.1%
0 809
7.4%

Branch
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size60.0 KiB
Alex
340 
Cairo
332 
Giza
328 

Length

Max length5
Median length4
Mean length4.332
Min length4

Characters and Unicode

Total characters4332
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlex
2nd rowGiza
3rd rowAlex
4th rowAlex
5th rowAlex

Common Values

ValueCountFrequency (%)
Alex 340
34.0%
Cairo 332
33.2%
Giza 328
32.8%

Length

2025-09-04T02:54:38.043551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T02:54:38.181639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
alex 340
34.0%
cairo 332
33.2%
giza 328
32.8%

Most occurring characters

ValueCountFrequency (%)
i 660
15.2%
a 660
15.2%
A 340
7.8%
e 340
7.8%
l 340
7.8%
x 340
7.8%
C 332
7.7%
r 332
7.7%
o 332
7.7%
G 328
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 660
15.2%
a 660
15.2%
A 340
7.8%
e 340
7.8%
l 340
7.8%
x 340
7.8%
C 332
7.7%
r 332
7.7%
o 332
7.7%
G 328
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 660
15.2%
a 660
15.2%
A 340
7.8%
e 340
7.8%
l 340
7.8%
x 340
7.8%
C 332
7.7%
r 332
7.7%
o 332
7.7%
G 328
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 660
15.2%
a 660
15.2%
A 340
7.8%
e 340
7.8%
l 340
7.8%
x 340
7.8%
C 332
7.7%
r 332
7.7%
o 332
7.7%
G 328
7.6%

City
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
Yangon
340 
Mandalay
332 
Naypyitaw
328 

Length

Max length9
Median length8
Mean length7.648
Min length6

Characters and Unicode

Total characters7648
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYangon
2nd rowNaypyitaw
3rd rowYangon
4th rowYangon
5th rowYangon

Common Values

ValueCountFrequency (%)
Yangon 340
34.0%
Mandalay 332
33.2%
Naypyitaw 328
32.8%

Length

2025-09-04T02:54:38.340421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T02:54:38.482761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
yangon 340
34.0%
mandalay 332
33.2%
naypyitaw 328
32.8%

Most occurring characters

ValueCountFrequency (%)
a 1992
26.0%
n 1012
13.2%
y 988
12.9%
Y 340
 
4.4%
o 340
 
4.4%
g 340
 
4.4%
M 332
 
4.3%
d 332
 
4.3%
l 332
 
4.3%
N 328
 
4.3%
Other values (4) 1312
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1992
26.0%
n 1012
13.2%
y 988
12.9%
Y 340
 
4.4%
o 340
 
4.4%
g 340
 
4.4%
M 332
 
4.3%
d 332
 
4.3%
l 332
 
4.3%
N 328
 
4.3%
Other values (4) 1312
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1992
26.0%
n 1012
13.2%
y 988
12.9%
Y 340
 
4.4%
o 340
 
4.4%
g 340
 
4.4%
M 332
 
4.3%
d 332
 
4.3%
l 332
 
4.3%
N 328
 
4.3%
Other values (4) 1312
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1992
26.0%
n 1012
13.2%
y 988
12.9%
Y 340
 
4.4%
o 340
 
4.4%
g 340
 
4.4%
M 332
 
4.3%
d 332
 
4.3%
l 332
 
4.3%
N 328
 
4.3%
Other values (4) 1312
17.2%

Customer type
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
Member
565 
Normal
435 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6000
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMember
2nd rowNormal
3rd rowNormal
4th rowMember
5th rowMember

Common Values

ValueCountFrequency (%)
Member 565
56.5%
Normal 435
43.5%

Length

2025-09-04T02:54:38.631327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T02:54:38.754417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
member 565
56.5%
normal 435
43.5%

Most occurring characters

ValueCountFrequency (%)
e 1130
18.8%
r 1000
16.7%
m 1000
16.7%
M 565
9.4%
b 565
9.4%
N 435
 
7.2%
o 435
 
7.2%
a 435
 
7.2%
l 435
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1130
18.8%
r 1000
16.7%
m 1000
16.7%
M 565
9.4%
b 565
9.4%
N 435
 
7.2%
o 435
 
7.2%
a 435
 
7.2%
l 435
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1130
18.8%
r 1000
16.7%
m 1000
16.7%
M 565
9.4%
b 565
9.4%
N 435
 
7.2%
o 435
 
7.2%
a 435
 
7.2%
l 435
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1130
18.8%
r 1000
16.7%
m 1000
16.7%
M 565
9.4%
b 565
9.4%
N 435
 
7.2%
o 435
 
7.2%
a 435
 
7.2%
l 435
 
7.2%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
Female
571 
Male
429 

Length

Max length6
Median length6
Mean length5.142
Min length4

Characters and Unicode

Total characters5142
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 571
57.1%
Male 429
42.9%

Length

2025-09-04T02:54:38.916256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T02:54:39.063074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 571
57.1%
male 429
42.9%

Most occurring characters

ValueCountFrequency (%)
e 1571
30.6%
a 1000
19.4%
l 1000
19.4%
F 571
 
11.1%
m 571
 
11.1%
M 429
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1571
30.6%
a 1000
19.4%
l 1000
19.4%
F 571
 
11.1%
m 571
 
11.1%
M 429
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1571
30.6%
a 1000
19.4%
l 1000
19.4%
F 571
 
11.1%
m 571
 
11.1%
M 429
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1571
30.6%
a 1000
19.4%
l 1000
19.4%
F 571
 
11.1%
m 571
 
11.1%
M 429
 
8.3%

Product line
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size73.9 KiB
Fashion accessories
178 
Food and beverages
174 
Electronic accessories
170 
Sports and travel
166 
Home and lifestyle
160 

Length

Max length22
Median length19
Mean length18.54
Min length17

Characters and Unicode

Total characters18540
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealth and beauty
2nd rowElectronic accessories
3rd rowHome and lifestyle
4th rowHealth and beauty
5th rowSports and travel

Common Values

ValueCountFrequency (%)
Fashion accessories 178
17.8%
Food and beverages 174
17.4%
Electronic accessories 170
17.0%
Sports and travel 166
16.6%
Home and lifestyle 160
16.0%
Health and beauty 152
15.2%

Length

2025-09-04T02:54:39.209583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T02:54:39.362643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
and 652
24.6%
accessories 348
13.1%
fashion 178
 
6.7%
food 174
 
6.6%
beverages 174
 
6.6%
electronic 170
 
6.4%
sports 166
 
6.3%
travel 166
 
6.3%
home 160
 
6.0%
lifestyle 160
 
6.0%
Other values (2) 304
11.5%

Most occurring characters

ValueCountFrequency (%)
e 2338
12.6%
a 1822
 
9.8%
s 1722
 
9.3%
1652
 
8.9%
o 1370
 
7.4%
c 1036
 
5.6%
r 1024
 
5.5%
n 1000
 
5.4%
t 966
 
5.2%
i 856
 
4.6%
Other values (15) 4754
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2338
12.6%
a 1822
 
9.8%
s 1722
 
9.3%
1652
 
8.9%
o 1370
 
7.4%
c 1036
 
5.6%
r 1024
 
5.5%
n 1000
 
5.4%
t 966
 
5.2%
i 856
 
4.6%
Other values (15) 4754
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2338
12.6%
a 1822
 
9.8%
s 1722
 
9.3%
1652
 
8.9%
o 1370
 
7.4%
c 1036
 
5.6%
r 1024
 
5.5%
n 1000
 
5.4%
t 966
 
5.2%
i 856
 
4.6%
Other values (15) 4754
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2338
12.6%
a 1822
 
9.8%
s 1722
 
9.3%
1652
 
8.9%
o 1370
 
7.4%
c 1036
 
5.6%
r 1024
 
5.5%
n 1000
 
5.4%
t 966
 
5.2%
i 856
 
4.6%
Other values (15) 4754
25.6%

Unit price
Real number (ℝ)

High correlation 

Distinct943
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.67213
Minimum10.08
Maximum99.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-04T02:54:39.562485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.08
5-th percentile15.279
Q132.875
median55.23
Q377.935
95-th percentile97.222
Maximum99.96
Range89.88
Interquartile range (IQR)45.06

Descriptive statistics

Standard deviation26.494628
Coefficient of variation (CV)0.4759047
Kurtosis-1.2185914
Mean55.67213
Median Absolute Deviation (MAD)22.505
Skewness0.0070774479
Sum55672.13
Variance701.96533
MonotonicityNot monotonic
2025-09-04T02:54:39.744959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.77 3
 
0.3%
98.7 2
 
0.2%
38.6 2
 
0.2%
93.96 2
 
0.2%
84.05 2
 
0.2%
20.01 2
 
0.2%
21.12 2
 
0.2%
32.32 2
 
0.2%
15.5 2
 
0.2%
99.96 2
 
0.2%
Other values (933) 979
97.9%
ValueCountFrequency (%)
10.08 1
0.1%
10.13 1
0.1%
10.16 1
0.1%
10.17 1
0.1%
10.18 1
0.1%
10.53 1
0.1%
10.56 1
0.1%
10.59 1
0.1%
10.69 1
0.1%
10.75 1
0.1%
ValueCountFrequency (%)
99.96 2
0.2%
99.92 1
0.1%
99.89 1
0.1%
99.83 1
0.1%
99.82 2
0.2%
99.79 1
0.1%
99.78 1
0.1%
99.73 1
0.1%
99.71 1
0.1%
99.7 1
0.1%

Quantity
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.51
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-04T02:54:39.900687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9234306
Coefficient of variation (CV)0.53056817
Kurtosis-1.2155472
Mean5.51
Median Absolute Deviation (MAD)2
Skewness0.012941048
Sum5510
Variance8.5464464
MonotonicityNot monotonic
2025-09-04T02:54:40.056445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 119
11.9%
1 112
11.2%
4 109
10.9%
7 102
10.2%
5 102
10.2%
6 98
9.8%
9 92
9.2%
2 91
9.1%
3 90
9.0%
8 85
8.5%
ValueCountFrequency (%)
1 112
11.2%
2 91
9.1%
3 90
9.0%
4 109
10.9%
5 102
10.2%
6 98
9.8%
7 102
10.2%
8 85
8.5%
9 92
9.2%
10 119
11.9%
ValueCountFrequency (%)
10 119
11.9%
9 92
9.2%
8 85
8.5%
7 102
10.2%
6 98
9.8%
5 102
10.2%
4 109
10.9%
3 90
9.0%
2 91
9.1%
1 112
11.2%

Sales
Real number (ℝ)

High correlation 

Distinct990
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.96675
Minimum10.6785
Maximum1042.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-04T02:54:40.230729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.6785
5-th percentile41.070225
Q1124.42238
median253.848
Q3471.35025
95-th percentile822.4965
Maximum1042.65
Range1031.9715
Interquartile range (IQR)346.92787

Descriptive statistics

Standard deviation245.88534
Coefficient of variation (CV)0.76133328
Kurtosis-0.081884758
Mean322.96675
Median Absolute Deviation (MAD)157.68375
Skewness0.8925698
Sum322966.75
Variance60459.598
MonotonicityNot monotonic
2025-09-04T02:54:40.419780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217.6335 2
 
0.2%
829.08 2
 
0.2%
263.97 2
 
0.2%
216.846 2
 
0.2%
276.948 2
 
0.2%
189.0945 2
 
0.2%
87.234 2
 
0.2%
175.917 2
 
0.2%
470.988 2
 
0.2%
93.744 2
 
0.2%
Other values (980) 980
98.0%
ValueCountFrequency (%)
10.6785 1
0.1%
12.6945 1
0.1%
13.167 1
0.1%
13.419 1
0.1%
14.679 1
0.1%
16.107 1
0.1%
16.2015 1
0.1%
16.275 1
0.1%
17.094 1
0.1%
18.6375 1
0.1%
ValueCountFrequency (%)
1042.65 1
0.1%
1039.29 1
0.1%
1034.46 1
0.1%
1023.75 1
0.1%
1022.49 1
0.1%
1022.385 1
0.1%
1020.705 1
0.1%
1003.59 1
0.1%
1002.12 1
0.1%
951.825 1
0.1%

Date
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size60.5 KiB

Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size47.0 KiB

Payment
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size62.8 KiB
Ewallet
345 
Cash
344 
Credit card
311 

Length

Max length11
Median length7
Mean length7.212
Min length4

Characters and Unicode

Total characters7212
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEwallet
2nd rowCash
3rd rowCredit card
4th rowEwallet
5th rowEwallet

Common Values

ValueCountFrequency (%)
Ewallet 345
34.5%
Cash 344
34.4%
Credit card 311
31.1%

Length

2025-09-04T02:54:40.664563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T02:54:40.815033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ewallet 345
26.3%
cash 344
26.2%
credit 311
23.7%
card 311
23.7%

Most occurring characters

ValueCountFrequency (%)
a 1000
13.9%
l 690
9.6%
e 656
9.1%
t 656
9.1%
C 655
9.1%
r 622
8.6%
d 622
8.6%
w 345
 
4.8%
E 345
 
4.8%
s 344
 
4.8%
Other values (4) 1277
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1000
13.9%
l 690
9.6%
e 656
9.1%
t 656
9.1%
C 655
9.1%
r 622
8.6%
d 622
8.6%
w 345
 
4.8%
E 345
 
4.8%
s 344
 
4.8%
Other values (4) 1277
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1000
13.9%
l 690
9.6%
e 656
9.1%
t 656
9.1%
C 655
9.1%
r 622
8.6%
d 622
8.6%
w 345
 
4.8%
E 345
 
4.8%
s 344
 
4.8%
Other values (4) 1277
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1000
13.9%
l 690
9.6%
e 656
9.1%
t 656
9.1%
C 655
9.1%
r 622
8.6%
d 622
8.6%
w 345
 
4.8%
E 345
 
4.8%
s 344
 
4.8%
Other values (4) 1277
17.7%

cogs
Real number (ℝ)

High correlation 

Distinct990
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.58738
Minimum10.17
Maximum993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-04T02:54:40.977106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.17
5-th percentile39.1145
Q1118.4975
median241.76
Q3448.905
95-th percentile783.33
Maximum993
Range982.83
Interquartile range (IQR)330.4075

Descriptive statistics

Standard deviation234.17651
Coefficient of variation (CV)0.76133328
Kurtosis-0.081884758
Mean307.58738
Median Absolute Deviation (MAD)150.175
Skewness0.8925698
Sum307587.38
Variance54838.638
MonotonicityNot monotonic
2025-09-04T02:54:41.165942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207.27 2
 
0.2%
789.6 2
 
0.2%
251.4 2
 
0.2%
206.52 2
 
0.2%
263.76 2
 
0.2%
180.09 2
 
0.2%
83.08 2
 
0.2%
167.54 2
 
0.2%
448.56 2
 
0.2%
89.28 2
 
0.2%
Other values (980) 980
98.0%
ValueCountFrequency (%)
10.17 1
0.1%
12.09 1
0.1%
12.54 1
0.1%
12.78 1
0.1%
13.98 1
0.1%
15.34 1
0.1%
15.43 1
0.1%
15.5 1
0.1%
16.28 1
0.1%
17.75 1
0.1%
ValueCountFrequency (%)
993 1
0.1%
989.8 1
0.1%
985.2 1
0.1%
975 1
0.1%
973.8 1
0.1%
973.7 1
0.1%
972.1 1
0.1%
955.8 1
0.1%
954.4 1
0.1%
906.5 1
0.1%

gross margin percentage
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
4.761904762
1000 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.761904762
2nd row4.761904762
3rd row4.761904762
4th row4.761904762
5th row4.761904762

Common Values

ValueCountFrequency (%)
4.761904762 1000
100.0%

Length

2025-09-04T02:54:41.389441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T02:54:41.511348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4.761904762 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
4 2000
18.2%
7 2000
18.2%
6 2000
18.2%
. 1000
9.1%
1 1000
9.1%
9 1000
9.1%
0 1000
9.1%
2 1000
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 2000
18.2%
7 2000
18.2%
6 2000
18.2%
. 1000
9.1%
1 1000
9.1%
9 1000
9.1%
0 1000
9.1%
2 1000
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 2000
18.2%
7 2000
18.2%
6 2000
18.2%
. 1000
9.1%
1 1000
9.1%
9 1000
9.1%
0 1000
9.1%
2 1000
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 2000
18.2%
7 2000
18.2%
6 2000
18.2%
. 1000
9.1%
1 1000
9.1%
9 1000
9.1%
0 1000
9.1%
2 1000
9.1%

gross income
Real number (ℝ)

High correlation 

Distinct990
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.379369
Minimum0.5085
Maximum49.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-04T02:54:41.645337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.5085
5-th percentile1.955725
Q15.924875
median12.088
Q322.44525
95-th percentile39.1665
Maximum49.65
Range49.1415
Interquartile range (IQR)16.520375

Descriptive statistics

Standard deviation11.708825
Coefficient of variation (CV)0.76133328
Kurtosis-0.081884758
Mean15.379369
Median Absolute Deviation (MAD)7.50875
Skewness0.8925698
Sum15379.369
Variance137.09659
MonotonicityNot monotonic
2025-09-04T02:54:41.829976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.3635 2
 
0.2%
39.48 2
 
0.2%
12.57 2
 
0.2%
10.326 2
 
0.2%
13.188 2
 
0.2%
9.0045 2
 
0.2%
4.154 2
 
0.2%
8.377 2
 
0.2%
22.428 2
 
0.2%
4.464 2
 
0.2%
Other values (980) 980
98.0%
ValueCountFrequency (%)
0.5085 1
0.1%
0.6045 1
0.1%
0.627 1
0.1%
0.639 1
0.1%
0.699 1
0.1%
0.767 1
0.1%
0.7715 1
0.1%
0.775 1
0.1%
0.814 1
0.1%
0.8875 1
0.1%
ValueCountFrequency (%)
49.65 1
0.1%
49.49 1
0.1%
49.26 1
0.1%
48.75 1
0.1%
48.69 1
0.1%
48.685 1
0.1%
48.605 1
0.1%
47.79 1
0.1%
47.72 1
0.1%
45.325 1
0.1%

Rating
Real number (ℝ)

Distinct61
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9727
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-04T02:54:42.044448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.295
Q15.5
median7
Q38.5
95-th percentile9.7
Maximum10
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7185803
Coefficient of variation (CV)0.24647271
Kurtosis-1.1515868
Mean6.9727
Median Absolute Deviation (MAD)1.5
Skewness0.0090096488
Sum6972.7
Variance2.9535182
MonotonicityNot monotonic
2025-09-04T02:54:42.225718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 26
 
2.6%
6.6 24
 
2.4%
9.5 22
 
2.2%
4.2 22
 
2.2%
5.1 21
 
2.1%
6.5 21
 
2.1%
8 21
 
2.1%
5 21
 
2.1%
6.2 21
 
2.1%
7.6 20
 
2.0%
Other values (51) 781
78.1%
ValueCountFrequency (%)
4 11
1.1%
4.1 17
1.7%
4.2 22
2.2%
4.3 18
1.8%
4.4 17
1.7%
4.5 17
1.7%
4.6 8
 
0.8%
4.7 12
1.2%
4.8 13
1.3%
4.9 18
1.8%
ValueCountFrequency (%)
10 5
 
0.5%
9.9 16
1.6%
9.8 19
1.9%
9.7 14
1.4%
9.6 17
1.7%
9.5 22
2.2%
9.4 12
1.2%
9.3 16
1.6%
9.2 16
1.6%
9.1 14
1.4%

Interactions

2025-09-04T02:54:35.189367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:31.451829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:32.249043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:32.938905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:33.660358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:34.430064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:35.311532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:31.573512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:32.364656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:33.052548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:33.780827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:34.549474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:35.452379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:31.697659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:32.472656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:33.169060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:33.918852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:34.677641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:35.590483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:31.815369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:32.586898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:33.284498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:34.055392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:34.800596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:35.827792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:31.932703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:32.699309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:33.404661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:34.191390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:34.926849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:35.959865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:32.049181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:32.816391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:33.529276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:34.311618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-04T02:54:35.056651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-09-04T02:54:42.500854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BranchCityCustomer typeGenderPaymentProduct lineQuantityRatingSalesUnit pricecogsgross income
Branch1.0001.0000.0000.0320.0000.0280.0000.0000.0000.0000.0000.000
City1.0001.0000.0000.0320.0000.0280.0000.0000.0000.0000.0000.000
Customer type0.0000.0001.0000.1300.0260.0000.0000.0000.0530.0910.0530.053
Gender0.0320.0320.1301.0000.0400.0210.0890.0800.0600.0780.0600.060
Payment0.0000.0000.0260.0401.0000.0000.0000.0000.0000.0400.0000.000
Product line0.0280.0280.0000.0210.0001.0000.0000.0000.0000.0000.0000.000
Quantity0.0000.0000.0000.0890.0000.0001.000-0.0150.7350.0110.7350.735
Rating0.0000.0000.0000.0800.0000.000-0.0151.000-0.017-0.008-0.017-0.017
Sales0.0000.0000.0530.0600.0000.0000.735-0.0171.0000.6301.0001.000
Unit price0.0000.0000.0910.0780.0400.0000.011-0.0080.6301.0000.6300.630
cogs0.0000.0000.0530.0600.0000.0000.735-0.0171.0000.6301.0001.000
gross income0.0000.0000.0530.0600.0000.0000.735-0.0171.0000.6301.0001.000

Missing values

2025-09-04T02:54:36.166545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-04T02:54:36.600936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Invoice IDBranchCityCustomer typeGenderProduct lineUnit priceQuantitySalesDateTimePaymentcogsgross margin percentagegross incomeRating
0750-67-8428AlexYangonMemberFemaleHealth and beauty74.697548.97152019-05-01 00:00:0013:08:00Ewallet522.834.76190526.14159.1
1226-31-3081GizaNaypyitawNormalFemaleElectronic accessories15.28580.22002019-08-03 00:00:0010:29:00Cash76.404.7619053.82009.6
2631-41-3108AlexYangonNormalFemaleHome and lifestyle46.337340.52552019-03-03 00:00:0013:23:00Credit card324.314.76190516.21557.4
3123-19-1176AlexYangonMemberFemaleHealth and beauty58.228489.04801/27/201920:33:00Ewallet465.764.76190523.28808.4
4373-73-7910AlexYangonMemberFemaleSports and travel86.317634.37852019-08-02 00:00:0010:37:00Ewallet604.174.76190530.20855.3
5699-14-3026GizaNaypyitawMemberFemaleElectronic accessories85.397627.61653/25/201918:30:00Ewallet597.734.76190529.88654.1
6355-53-5943AlexYangonMemberFemaleElectronic accessories68.846433.69202/25/201914:36:00Ewallet413.044.76190520.65205.8
7315-22-5665GizaNaypyitawMemberFemaleHome and lifestyle73.5610772.38002/24/201911:38:00Ewallet735.604.76190536.78008.0
8665-32-9167AlexYangonMemberFemaleHealth and beauty36.26276.14602019-10-01 00:00:0017:15:00Credit card72.524.7619053.62607.2
9692-92-5582CairoMandalayMemberFemaleFood and beverages54.843172.74602/20/201913:27:00Credit card164.524.7619058.22605.9
Invoice IDBranchCityCustomer typeGenderProduct lineUnit priceQuantitySalesDateTimePaymentcogsgross margin percentagegross incomeRating
990886-18-2897AlexYangonNormalFemaleFood and beverages56.565296.94003/22/201919:06:00Credit card282.804.76190514.14004.5
991602-16-6955CairoMandalayNormalFemaleSports and travel76.6010804.30001/24/201918:10:00Ewallet766.004.76190538.30006.0
992745-74-0715AlexYangonNormalMaleElectronic accessories58.032121.86302019-10-03 00:00:0020:46:00Ewallet116.064.7619055.80308.8
993690-01-6631CairoMandalayNormalMaleFashion accessories17.4910183.64502/22/201918:35:00Ewallet174.904.7619058.74506.6
994652-49-6720GizaNaypyitawMemberFemaleElectronic accessories60.95163.99752/18/201911:40:00Ewallet60.954.7619053.04755.9
995233-67-5758GizaNaypyitawNormalMaleHealth and beauty40.35142.36751/29/201913:46:00Ewallet40.354.7619052.01756.2
996303-96-2227CairoMandalayNormalFemaleHome and lifestyle97.38101022.49002019-02-03 00:00:0017:16:00Ewallet973.804.76190548.69004.4
997727-02-1313AlexYangonMemberMaleFood and beverages31.84133.43202019-09-02 00:00:0013:22:00Cash31.844.7619051.59207.7
998347-56-2442AlexYangonNormalMaleHome and lifestyle65.82169.11102/22/201915:33:00Cash65.824.7619053.29104.1
999849-09-3807AlexYangonMemberFemaleFashion accessories88.347649.29902/18/201913:28:00Cash618.384.76190530.91906.6